IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

Xi Yang1 Ding Xia1,2 Taichi Kin1 Takeo Igarashi1

1The University of Tokyo 2South China University of Technology

Figure 1: 3D models of intracranial aneurysm segments with segmentation annotation in our dataset. Hot pink shows the

healthy blood vessel part, and aqua shows the aneurysm part for each model.

Abstract

Medicine is an important application area for deep

learning models. Research in this field is a combina-

tion of medical expertise and data science knowledge. In

this paper, instead of 2D medical images, we introduce

an open-access 3D intracranial aneurysm dataset, IntrA,

that makes the application of points-based and mesh-based

classification and segmentation models available. Our

dataset can be used to diagnose intracranial aneurysms and

to extract the neck for a clipping operation in medicine

and other areas of deep learning, such as normal estima-

tion and surface reconstruction. We provide a large-scale

benchmark of classification and part segmentation by test-

ing state-of-the-art networks. We also discuss the perfor-

mance of each method and demonstrate the challenges of

our dataset. The published dataset can be accessed here:

https://github.com/intra3d2019/IntrA.

1. Introduction

Intracranial aneurysm is a life-threatening disease, and

its surgical treatments are complicated. Timely diagnosis

and preoperative examination are necessary to formulate

the treatment strategies and surgical approaches. Currently,

the primary treatment method is clipping the neck of an

aneurysm to prevent it from rupturing, as shown in Figure 2.

The decisions of the position and posture of the clip are still

highly dependent on “clinical judgment” based on the ex-

perience of physicians. In the surgery support system of in-

tracranial aneurysms simulating real-life neurosurgery and

teach neurosurgical residents [5], the accuracy of aneurysm

segmentation is the most crucial part because it is used to

extract the neck of an aneurysm, that is, the boundary line

of the aneurysm.

Based on 3D surface models, the diagnosis of an

aneurysm can be much more accurate than 2D images. The

edge of the aneurysm is much clearer for doctors, and the

complicated and time-consuming annotation of a mess of

2D images is avoided. There are many reports of automatic

diagnosis and segmentation of aneurysms based on medical

images, including intracranial aneurysm (IA) and abdomi-

nal aortic aneurysm (AAA) [24, 30, 37]; however, few re-

ports have been published based on 3D models. This is not

only because data collection is inefficient, subjective, and

2656

Figure 2: The treatment of intracranial aneurysm by clip-

ping.

challenging to share in medicine, but also the joint knowl-

edge of computer application science and medical science.

Objects with arbitrary shapes are ubiquitous, and a non-

Euclidean manifold reveals more critical information than

using Euclidean geometry, like complex typologies of brain

tissues in neuroscience[7]. However, the study of 2D mag-

netic resonance angiography (MRA) images confines the

selection to 3D neural networks based on pixels and voxels,

which also omits the information from manifolds. There-

fore, we propose an open-access 3D intracranial aneurysm

dataset to solve the above issues and to promote the applica-

tion of deep learning models in medical science. The points

and meshes-based models exhibit excellent generalization

abilities for 3D deep learning tasks in our experiments.

Our main contributions are:

1. We propose an open dataset that consists of 3D

aneurysm segments with segmentation annotations,

automatically generated blood vessel segments, and

complete models of scanned blood vessels of the

brains. All annotated aneurysm segments are pro-

cessed as manifold meshes.

2. We develop tools to generate 3D blood vessel segments

from complete models and to annotate a 3D aneurysm

model interactively. The data processing pipeline is

also introduced.

3. We evaluate the performance of various state-of-the-

art 3D deep learning methods on our dataset to pro-

vide benchmarks of classification (diagnose) and seg-

mentation of intracranial aneurysms. Furthermore, we

analyze the different features of each method from the

results obtained.

2. Related Work

2.1. Datasets

Medical dataset. Large-scale samples are required to

surmount the challenges of the complexity and heterogene-

ity of many diseases, but data collection in medical research

is costly, it is unattainable for a single research institute.

Therefore, data sharing is critical. Several medical datasets

have been published online for collaboration on finding a

treatment solution. For example, integrated dataset Med-

Pix [4], bone X-rays dataset MURA [35], brain neuroimag-

ing dataset [15], Medical Segmentation Decathlon [38],

Harvard GSP [8], and SCR database [42]. Data collec-

tion is also critical for a single category of disease, such as,

The Lung Image Database Consortium (LIDC-IDRI) [3],

Indian Diabetic Retinopathy Image Dataset (IDRiD) [2],

EyePACS [2], and Autism Brain Imaging Data Exchange

(ABIDE) [1]. To date, almost all of them are 2D medical

images.

Non-medical 3D dataset. In recent years, 3D model

datasets were introduced in the research of computer vi-

sion and computer graphics with the development of deep

learning algorithms. For instance, CAD model datasets:

modelNet [48], shapeNet [9], COSEG Dataset [44], ABC

dataset [23]; 3D printing model datasets: Thingi10K [52],

Human model dataset [31], etc. Various 3D deep learning

tasks are widely carried out on these datasets.

2.2. Methods

A 3D model has four kinds of representations, projected

view, voxel, point cloud, and mesh. The methods based

on projected view or voxel are implemented conveniently

using similar structures with 2D convolutional neural net-

works (CNNs). Point cloud or mesh has a more accurate

representation of a 3D shape; however, new convolution

structures are required.

Projected View. Su et al. proposed a multi-view CNN

to recognize 3D shapes [40]. Kalogerakis et al. com-

bined image-based fully convolutional networks (FCNs)

and surface-based conditional random fields (CRFs) to yield

coherent segmentation of 3D shapes [21].

Voxel. Cicek et al. introduced 3D U-Net for volumetric

segmentation that learns from sparsely annotated volumet-

ric images [10]. Wang et al. presented O-CNN, an Octree-

based Convolutional Neural Network (CNN), for 3D shape

analysis [43]. Graham et al. designed new sparse convolu-

tional operations to process spatially-sparse 3D data, called

submanifold sparse convolutional networks (SSCNs) [16].

Wang and Lu proposed VoxSegNet to extract discrimina-

tive features encoding detailed information under limited

resolution [46]. Le and Duan proposed the PointGrid, a

3D convolutional network that is an integration of point and

grid [26].

Points. Qi et al. proposed PointNet, making it is possible

to input 3D points directly for neural work [33], then they

introduced a hierarchical network PointNet++ to learn local

features [34]. Based on these pioneering works, many new

convolution operations were proposed. Wu et al. treated

convolution kernels as nonlinear functions of the local coor-

2657

Complete model (103) Generated segments (1909) Annotated segments (116)

Figure 3: There are three types of data in our dataset. The automatically generated blood vessel segments can be with or

without aneurysms, and an aneurysm is often partially separated. We see that both aneurysm segments and healthy vessel

segments have complex shapes with a different number of branches. For example, in the annotated segments, the aneurysm

is huge in the second segment of the first row, but it is tiny in the first segment of the third row. The second segment of the

third row has even two aneurysms.

dinates of 3D points comprised of weight and density func-

tions, named PointConv [47]. Li et al. presented PointCNN

that can leverage spatially local correlation in data repre-

sented densely in grids for feature learning [28]. Xu et al.

designed the filter as a product of a simple step function that

captures local geodesic information and a Taylor polyno-

mial, named SpiderCNN [49]. Moreover, the SO-Net mod-

els the spatial distribution of point cloud by building a Self-

Organizing Map (SOM) [27]. Su et al. presented SPLAT-

Net for processing point clouds that directly operates on a

collection of points represented as a sparse set of samples

in a high-dimensional lattice [39]. Zhao et al. proposed 3D

point-capsule networks [51]. Wang et al. proposed dynamic

graph neural network (DGCNN) [45]. Yang et al. devel-

oped Point Attention Transformers (PATs) to process the

point clouds [50]. Thomas et al. presented a new design of

point convolution, called Kernel Point Convolution1 (KP-

Conv) [41]. Liu et al. proposed a dynamic points agglomer-

ation module to construct an efficient hierarchical point sets

learning architecture. [29].

Mesh. Maron et al. applied a convolution operator to

sphere-type shapes using a global seamless parameteriza-

tion to a planar flat-torus [31]. Hanocka et al. utilize the

unique properties of the triangular mesh for direct analysis

of 3D shapes, named MeshCNN [17]. Feng et al. regard the

polygon faces as the unit, split their features into spatial and

structural features called MeshNet [14].

3. Our Dataset

3.1. Data

Our dataset includes complete models with aneurysms,

generated vessel segments, and annotated aneurysm seg-

ments, as shown in Figure 3. 103 3D models of entire

brain vessels are collected by reconstructing scanned 2D

MRA images of patients. We do not publish the raw 2D

MRA images because of medical ethics. 1909 blood ves-

sel segments are generated automatically from the complete

models, including 1694 healthy vessel segments and 215

aneurysm segments for diagnosis. An aneurysm can be di-

vided into segments that can verify the automatic diagnosis.

116 aneurysm segments are divided and annotated manu-

ally by medical experts; the scale of each aneurysm seg-

ment is based on the need for a preoperative examination.

The details are described in the next section. Furthermore,

geodesic distance matrices are computed and included for

each annotated 3D segment, because the expression of the

geodesic distance is more accurate than Euclidean distance

according to the shape of vessels. The matrix is saved as

N × N for a model with N points, shortening the training

computation time.

Our data have several characteristics common to med-

ical data: 1) Small but diverse. The amount of data

is not so large compared to other released CAD model

datasets; however, it includes diverse shapes and scales of

intracranial aneurysms as well as different amounts of ves-

2658

Figure 4: An intracranial aneurysm is restored from incom-

plete scanned data by neurosurgeons.

sel branches. 2) Unbalanced. The number of points of

aneurysms and healthy vessel parts is imbalanced based on

the shape of aneurysms. The number of 3D aneurysm seg-

ments and healthy vessel segments are not equal because the

aneurysms are usually much smaller than the entire brain.

Challenge. Experts collected out dataset instead of regu-

lar people. Intact 3D models have to be restored from recon-

structed data manually, as shown in Figure 4. Besides, the

annotation of the neck of aneurysms necks requires years

of clinical experience in complex situations. Also, the 3D

models are not manifold. We clean the surface meshes to

create an ideal dataset for algorithm research.

Statistics and analysis. The statistics of our dataset

are shown in Figure 5. We count the number points in

each segment to some extent express the difference of the

shapes, since the points are mostly uniformly distributed on

the surface. The number of points generated in 1909 seg-

ments is approximately 500 to 1700 at Geodesic distance

30. Our dataset includes all types of intracranial aneurysms

in medicine: bifurcation type, trunk type, blister type, and

combined type. The shapes of aneurysm are diverse in

our dataset both in geometry and topology; six aneurysm

segments selected are shown in the right of Figure 3. Be-

sides, we calculated the size of an aneurysm as the ratio be-

tween the diagonal distance of the global segment and the

aneurysm part instead of the real size of the parent vessel or

the aneurysm.

3.2. Tools

We developed annotation tools and segment generation

tools to assist in constructing our dataset.

Annotation. Users draw an intended boundary by click-

ing several points. The connection between two points is

determined by the shortest path. After users create a closed

boundary line, they annotate the aneurysm part by select-

ing a point inside of it. The enclosed area is calculated au-

tomatically by propagation from the point to the boundary

line along with surface meshes. With the support of multi-

ple boundary lines, the annotation tool also can be used for

separating both the aneurysm part and the aneurysm seg-

ment manually, as shown in Figure 6.

Vessel segment generation. Vessel segments are gen-

erated by randomly picking points from the complete mod-

els and selecting the neighbor area whose geodesic distance

along the vessel is smaller than a threshold. We also man-

ually select points for increasing the number of segments

with an aneurysm. To construct an ideal dataset, few data

which are ambiguous or only include trivial components are

removed by using our visualization tool.

3.3. Processing pipeline

The processing pipeline is shown in Figure 7, more de-

tails are described in the supplementary material.

3D reconstruction and restore. Our data are ac-

quired by Time-Of-Flight Magnetic Resonance Angiogra-

phy (TOF-MRA) of human brain. Using the single thresh-

old method [19], each complete 3D model is reconstructed

from 512 × 512 × 180 ∼ 300 2D images sliced by

0.469 × 0.469 × 1mm. The aneurysm segments are sep-

arated and restored interactively using the multi-threshold

method [22] by two neurosurgeons, then processed by

Gaussian smoothing. This image processing is conducted

in life sciences software, Amira 2019 (Thermo Fisher Sci-

entific, MA, USA). It takes about 50 workdays in total.

Generation and annotation. By using our generation

and annotation tools, blood vessel segments are obtained

and classified. The segmentation annotation of aneurysm

segments is also finished. A neurosurgeon completed it in 8

hours.

Data clean and re-meshing. The reconstructed 3D

models are noisy and not manifold. Huang et al. [20] de-

scribed an algorithm to generate a manifold surface for

3D models; however, this method does not remove iso-

lated components and significantly changes the shape of the

model. Therefore, we use filter tools in MeshLab to remove

duplicate faces and vertices, and separate pieces in the data

manually, which ensure that the models do not have non-

manifold edges. MeshLab also generates the normal vector

at each point. The geodesic matrix is computed by solving

the heat equation on the surface using a fast approximate

geodesic distance method by [11].

3.4. Supported Studies

Diagnose (Classification). The diagnosis of an

aneurysm can be considered as a classification problem of

aneurysms and healthy vessel segments. From a 3D brain

model of a patient, vessel segments are generated by our

tools; then, the diagnosis is completed by classifying the

segments with aneurysms.

Part segmentation. Our annotated 3D models present a

precise boundary of each aneurysm to support segmentation

research. The data is easy to convert to any 3D representa-

tion of various deep learning algorithms.

Rule-based algorithms. Besides, algorithms based on

rules are proposed for either aneurysm in the brain or ab-

2659

Figure 5: Statistics of annotated models.

Figure 6: The top figure shows the UI of our annotation tool.

The points the user clicked are shown as blue to decide the

boundary of the aneurysm, then a random point (yellow)

is selected to annotate the aneurysm part (green). The two

bottom figures demonstrate the results of multiple boundary

lines.

dominal aortic aneurysm [13, 25, 36]. The accuracy and

generalization of the designed rules should be verified by

large data.

Others. Because we provide the information about the

normal vector and geodesic distance in our dataset, other

researches on 3D models are also supported, e.g. normal

estimation [6], geodesic estimation [18], 3D surface recon-

struction [12, 32], and etc.

4. Benchmark

We selected state-of-the-art methods as the benchmarks

of classification and segmentation of our dataset. We im-

plemented dataset interfaces to the original implementa-

tions by the authors and kept the same hyper-parameters

and loss functions of models as in the original papers. A

detailed explanation of the implementation of each method

is described in the supplementary material. We tested

these methods by 5-fold cross-validation. The shuffled data

was divided into 5 subsamples and was the same for each

method. 4 subsamples were used as training data, 1 was for

test data. The experiments were carried out on PCs with

GeForce RTX 2080 Ti ×2, GeForce GTX 1080 Ti ×1. The

net training time of all methods was over 92 hours.

4.1. Classification

6 methods were selected for binary classification bench-

marks, including PointNet [33], PointNet++ (PN++) [34],

PointCNN [28], SpiderCNN [49], self-organizing network

(SO-Net) [27], dynamic graph CNN (DGCNN) [45]. We

combined the generated blood vessel segments and man-

ually segmented aneurysms in total 2025 as the dataset

for testing classification accuracy and F1-Score of each

method. The experimental results are shown in Table 1.

PN++ with 1024 sampling points has the highest ac-

curacy of aneurysms, and PointCNN with 2048 sampled

points has the greatest accuracy of artery and F1-Score.

The accuracy and F1-score of almost all methods showed

an increasing tendency as more sampled points were pro-

vided. However, SpiderCNN attained the highest aneurysm

detection rate and F1-Score at 1024 sampling points. The

majority of misclassified 3D models contained small-sized

or incomplete aneurysms that are hard to distinguish from

healthy blood vessels.

4.2. Segmentation

We selected 11 networks, PointGrid [26], two kind of

submanifold sparse convolutional networks (SSCNs): fully-

connected (SSCN-F) and Unet-like (SSCN-U) [16] struc-

tures, PointNet [33], two kind of PointNet++ [34]: input

with normal (PN++) and with normal and geodesic dis-

tance (PN++g), PointConv [47], PointCNN [28], Spider-

CNN [49], MeshCNN [17], and SO-Net [27], to provide

segmentation benchmarks. 116 annotated aneurysm seg-

ments were used for evaluating these methods. The test of

2660

Figure 7: Pipeline of data processing. We generate 3D surface models of brain blood vessels from MRA images. Then, the

segments are generated and annotated. Data clean and re-meshing are conducted as the requirement of mesh-based methods.

Network Input V. (%). A. (%) F1-Score

PN++

512 98.52 86.69 0.8928

1024 98.52 88.51 0.9029

2048 98.76 87.31 0.9016

SpiderCNN

512 98.05 84.58 0.8692

1024 97.28 87.90 0.8722

2048 97.82 84.89 0.8662

SO-Net

512 98.76 84.24 0.8840

1024 98.88 81.21 0.8684

2048 98.88 83.94 0.8850

PointCNN

512 98.38 78.25 0.8494

1024 98.79 81.28 0.8748

2048 98.95 85.81 0.9044

DGCNN

512/10 95.22 60.73 0.6578

1024/20 95.34 72.21 0.7376

2048/40 97.93 83.40 0.8594

PointNet

512 94.45 67.66 0.6909

1024 94.98 64.96 0.6835

2048 93.74 69.50 0.6916

Table 1: Classification results of each method. The second

column shows the number of input points, the additional

input K is required for DGCNN. The accuracies of healthy

vessel segments (V.) and aneurysm segments (A.) and F1-

Score are calculated by the mean value of each fold.

each subsample was repeated 3 times, and the final results

were the mean values of each best result. We assessed the

effects of each method using two indexes: Jaccard Index

(JI) and Sørensen-Dice Coefficient (DSC). Jaccard Index is

also known as Intersection over Union (IoU). The results of

segmentation are shown in Figure 8 and Table 2.

Methods based on points. The segmentation meth-

ods based on point cloud obtained good results and main-

tained the same level with the results on ShapeNet [9]. SO-

Net showed excellent performance on IOU and DSC of

aneurysms, while PointConv had the best result on parent

blood vessels. PN++ had the third-best performance and

had the fastest training speed (5s per epoch, and converged

at approximately an epoch of 115 on GTX 1080 Ti). Mean-

while, PointCNN had the slowest training speed (24s per

epoch, and converged at approximately an epoch of 500 on

GTX 1080 Ti) and moderate segmentation accuracy. Spi-

derCNN did not have the same performance as it had on

the ShapeNet, but CI95 was unusually high. Besides the

methods mentioned in Section 4.2, we also tried 3D Cap-

suleNet [51], but it classified every point into the healthy

blood vessel, which shows its limited generalization cross-

ing datasets.

Resolution of voxels. Methods based on voxels

achieved relatively low IOU and DSC on each fold. The

performance of SSCN grew as the resolution was increased

from 24 to 40 (the resolution 24 was offered by the authur

in the code). But the average IOU had a fluctuation of about

8%, which was quite obvious compared to other methods

(about 2%). Based on the paper of PointGrid, N = 16 and

K = 2 were recommended parameters. However, we no-

ticed that the combination of N = 32 and K = 2 achieved

the highest scores.

Common poorly segmented 3D models. Most mod-

els were segmented excellent as top two rows in Figure 9.

However, the accuracy dropped when the aneurysm occu-

pied a small size ratio of the segment, like the third and

fourth row. Meanwhile, the segmentation performance of

aneurysms with a large size ratio was satisfactory. The fifth

row shows a special segment with 2 aneurysms. Although

most of methods failed to segment it, PointConv and PN++

with geodesic information maintained a good performance.

Geodesic information. Compared to other CAD model

datasets, the complex shapes of blood vessels is a different

challenge in part segmentation. Methods based on points

usually use the Euclidean distance to estimate the relevance

between points. However, it is not ideal for our dataset. For

example, PN++ misclassified the aneurysm points close to

the blood vessels even with normal information, as shown in

the last row of Figure 9. While, by using geodesic distance,

2661

Figure 8: IoU results of the aneurysm part for each fold of networks. These methods are compared with their best perfor-

mance.

Goundtruth PointConv PN++g PN++

Figure 9: Results comparison of three networks. More de-

tails are described in the supplementary material.

PN++ learned more exact spatial structure. PointConv also

segmented it well. Its excellent performance can be at-

tributed to the network learning the parameters of spatial

filters. In addition, MeshCNN segmented every aneurysm

decently although the overall performance is not best, which

owes to its convolution on meshes providing information on

manifolds.

5. Conclusion and Further Work

In this paper, we introduced a 3D dataset of intracranial

aneurysm with annotation by experts for geometric deep

learning networks. The developed tools and data process-

ing pipeline is also released. Furthermore, we evaluated and

analyzed the state-of-the-art methods of 3D object classifi-

cation and part segmentation on our dataset. The existing

methods are likely to be less effective on complex objects,

though they perform well on the segmentation of common

ones. It is possible to improve further the performance and

generalization of networks when geodesic or connectivity

information on 3D surfaces is accessible. The introduction

of our dataset can be instructive to the development of new

structures of geometric deep learning methods for medical

datasets.

In further work, we will keep increasing processed real

data for our dataset. Besides, we will verify the feasibility

of synthetic data for data augmentation, which can signifi-

cantly improve the efficiency of data collection. We hope

more deep learning networks will be applied to medical

practice.

Acknowledgements

This research was supported by AMED under Grant

Number JP18he1602001.

2662

Network InputIoU (%) CI 95 (%) DSC (%) CI 95 (%)

V. A. V. A. V. A. V. A.

Po

int

SO-Net

512 94.22 80.14 92.12 ∼ 96.32 73.71 ∼ 86.57 96.95 87.90 95.79 ∼ 98.12 83.14 ∼ 92.66

1024 94.42 80.99 92.39 ∼ 96.45 74.71 ∼ 87.27 97.06 88.41 95.93 ∼ 98.19 83.65 ∼ 93.17

2048 94.46 81.40 92.51 ∼ 96.41 75.37 ∼ 87.43 97.09 88.76 96.02 ∼ 98.16 84.38 ∼ 93.15

Po

int

PointConv

512 94.16 79.09 91.76 ∼ 96.56 70.26 ∼ 87.92 96.89 86.01 95.55 ∼ 98.24 78.34 ∼ 93.69

1024 94.59 79.42 92.53 ∼ 96.66 70.55 ∼ 88.29 97.15 86.29 96.00 ∼ 98.30 78.33 ∼ 94.25

2048 94.65 79.53 92.64 ∼ 96.67 70.96 ∼ 88.10 97.18 86.52 96.06 ∼ 98.30 78.95 ∼ 94.09

Po

int

PN++g

512 93.34 75.74 91.07 ∼ 95.60 66.51 ∼ 84.97 96.47 83.90 95.19 ∼ 97.74 75.68 ∼ 92.12

1024 93.28 76.53 90.93 ∼ 95.62 67.91 ∼ 85.15 96.43 84.82 95.12 ∼ 97.75 77.65 ∼ 91.99

2048 93.60 76.95 91.44 ∼ 95.75 68.76 ∼ 85.14 96.62 85.18 95.41 ∼ 97.82 78.39 ∼ 91.98

Po

int

PN++

512 93.42 76.22 90.91 ∼ 95.92 66.70 ∼ 85.73 96.48 83.92 95.04 ∼ 97.92 75.46 ∼ 92.38

1024 93.35 76.38 91.10 ∼ 95.60 67.96 ∼ 84.80 96.47 84.62 95.20 ∼ 97.74 77.45 ∼ 91.78

2048 93.24 76.21 90.93 ∼ 95.56 67.99 ∼ 84.43 96.40 84.64 95.08 ∼ 97.72 77.71 ∼ 91.57

Po

int

PointCNN

512 92.49 70.65 89.77 ∼ 95.22 58.89 ∼ 82.42 95.97 78.55 94.41 ∼ 97.54 67.37 ∼ 89.73

1024 93.47 74.11 91.11 ∼ 95.84 63.54 ∼ 84.68 96.53 81.74 95.20 ∼ 97.86 71.88 ∼ 91.59

2048 93.59 73.58 91.45 ∼ 95.73 62.81 ∼ 84.35 96.62 81.36 95.43 ∼ 97.80 71.39 ∼ 91.33

Mes

h

MeshCNN

750 85.43 55.63 81.22 ∼ 89.64 46.53 ∼ 64.73 91.71 68.65 88.86 ∼ 94.56 60.16 ∼ 77.14

1500 90.86 71.32 88.20 ∼ 93.53 65.01 ∼ 77.62 95.10 82.21 93.55 ∼ 96.64 77.26 ∼ 87.16

2250 90.34 71.60 87.34 ∼ 93.34 63.99 ∼ 79.21 94.77 81.87 93.01 ∼ 96.53 75.72 ∼ 88.01

Po

int

SpiderCNN

512 90.16 67.25 86.34 ∼ 93.98 55.21 ∼ 79.29 94.53 75.82 92.17 ∼ 96.88 64.07 ∼ 87.56

1024 87.95 61.60 83.65 ∼ 92.24 48.97 ∼ 74.23 93.24 71.08 90.56 ∼ 95.93 58.50 ∼ 83.67

2048 87.02 58.32 82.57 ∼ 91.47 45.21 ∼ 71.44 92.71 67.74 89.94 ∼ 95.47 54.50 ∼ 80.98

Vo

xel

SSCN-F

24 87.95 56.56 84.32 ∼ 91.57 43.29 ∼ 69.84 93.35 66.04 91.14 ∼ 95.56 52.28 ∼ 79.80

32 88.08 55.26 84.62 ∼ 91.54 41.39 ∼ 69.13 93.44 64.05 91.36 ∼ 95.53 49.45 ∼ 78.66

40 90.09 61.45 87.00 ∼ 93.17 48.54 ∼ 74.37 94.62 70.54 92.78 ∼ 96.46 57.16 ∼ 83.91

Vo

xel

SSCN-U

24 87.43 55.78 83.48 ∼ 91.37 42.07 ∼ 69.48 93.00 65.03 90.58 ∼ 95.43 50.91 ∼ 79.16

32 86.13 53.52 82.12 ∼ 90.13 40.64 ∼ 66.40 92.24 64.01 89.72 ∼ 94.76 50.95 ∼ 77.06

40 88.66 57.94 85.25 ∼ 92.07 44.92 ∼ 70.96 93.78 67.39 91.73 ∼ 95.83 54.09 ∼ 80.64

Vo

xel

PointGrid

16/2 78.32 35.82 73.53 ∼ 83.12 25.22 ∼ 46.42 87.36 47.33 84.12 ∼ 90.60 35.28 ∼ 59.38

16/4 79.49 38.23 74.54 ∼ 84.44 26.29 ∼ 50.17 88.08 49.14 84.73 ∼ 91.43 35.65 ∼ 62.64

32/2 80.11 42.42 75.60 ∼ 84.62 30.51 ∼ 54.34 88.50 53.52 85.54 ∼ 91.45 40.41 ∼ 66.63

Po

int

PointNet

512 73.99 37.30 67.43 ∼ 80.56 26.17 ∼ 48.44 84.05 48.96 79.31 ∼ 88.79 36.53 ∼ 61.38

1024 75.23 37.07 69.10 ∼ 81.36 25.66 ∼ 48.48 85.00 48.38 80.69 ∼ 89.31 35.63 ∼ 61.13

2048 74.22 37.75 67.85 ∼ 80.60 26.85 ∼ 48.64 84.17 49.59 79.56 ∼ 88.78 37.48 ∼ 61.70

Table 2: Segmentation results of each network. The second column shows the number of input points, edges, or resolutions

for the methods based on points, mesh, and voxel, respectively. For PointGrid, the additional refers to the parameter K in

the paper. The healthy vessel part and aneurysm part are noted as V. and A., respectively. CI95 indicated 95% confidence

interval of IoU or DSC. The results are calculated by the mean value of each fold.

2663

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